Two identical copies of a computer program listing appendix have been filed in conjunction with the present application and are hereby incorporated by reference in their entireties into the present disclosure. Each copy is on a CD-R medium compatible with an IBM PC running MS Windows. The computer program listing appendix includes the following files:
Both copies of the computer program listing appendix were created Oct. 16, 2001. The creation date of the files on the CD-R media reflects the date of creation of the media, not the date of origination of the programs listed in the files.
The present invention relates to a system and method for classification of air masses and more specifically to an improvement to the spatial synoptic classification.
Synoptic weather-typing, or the classification of weather conditions or patterns into categories, is an endeavor which has been undertaken numerous times within the past century, with many different methodologies, techniques, goals, and results. The reasons for synoptic classification are twofold: It is a tool for improved understanding of the climate system, and it is useful for climate impact applications. It is largely for this second reason that synoptic weather-typing has flourished once again during the past two decades; increased concern over the impacts of weather, especially for the purpose of understanding possible implications of climate change, have driven the search for more, and better, weather-typing schemes.
Synoptic climatology has been defined as a deductive science which integrates the simultaneous atmospheric dynamics and coupled response of the surface environment. While the atmospheric dynamics and the surface environment are individually studied by atmospheric scientists and members of many other sciences, only in synoptic climatology are the relationships between the two the focus of study. The synoptic climatologist usually employs statistical rather than mathematical analysis; as a result, the researcher forsakes individual atmospheric dynamic effects for the holistic-effect of the atmosphere. Indeed, it is this aggregate of conditions with which the surface environment coexists and interacts.
Classification is a rudiment of synoptic climatology; modern classification schemes can be traced back to common origins in the early part of the 20th century. There are nearly as many ways of classifying classification schemes as there arc schemes. For example, schemes have been subdivided on the basis of scale: local, regional, or global; or on the basis of methodology: objective, subjective, and multi-stage objective (which implies subjective decisions made amidst objective stages); or into subjective and automated (a more appropriate term than objective) methods. Some methods are circulation-to-environment, implying classification is done first and applied to environment later; these contrast with environment-to-circulation methods which account for environmental concerns in their classification methodology. Weather types (or air mass designations) are the goal of some schemes; map-pattern classifications of others.
Before the advent of high-speed computers, virtually all synoptic methods were subjective, or manual. Much early work, before the wide availability of upper air data, centered on air mass identification. An air mass is a contiguous and relatively homogeneous volume of air with respect to its thermal profile and moisture characteristics. Frontal theory, first promoted by the xe2x80x9cBergen schoolxe2x80x9d of meteorologists after the end of World War I, led to the first widely used, and best-known air mass identification system. Four main air masses affecting the middle latitudes were identified: continental polar (cP), continental tropical (cT), maritime polar (mP), and maritime tropical (mT). These airmass monikers, with various modifications and refinements, have appeared in introductory meteorological textbooks to this day.
Manual techniques have gone far beyond these simple theoretical designations. The Muller Classification developed a system which has proven useful for a variety of applications, from insect populations to air quality. That system, set up for New Orleans, La., but easily extrapolated to much of the Gulf Coast, identifies eight distinct sea-level pressure and front patterns typically found in the region (e.g. xe2x80x9cContinental Highxe2x80x9d, xe2x80x9cGulf Returnxe2x80x9d, xe2x80x9cTropical Cyclonexe2x80x9d). Updates are continually performed, and the calendar is complete from 1951.
The Lamb Catalogue is another famous weather-typing scheme, designed for sea-level pressure patterns over the United Kingdom, but as with Muller""s system, transferable elsewhere. The system contains 27 different classifications, combinations of the direction of wind flow (eight cardinal points and unclassifiable) and curvature of wind flow (cyclonic, anticyclonic, or neither). The system has been used for numerous applications, including temperature forecasting and rainfall acidity.
Developed for Central Europe, Grosswetterlagen differs from the above two systems in that it examines several-day-long patterns first and then divides them into individual days. The four main categories are zonal, meridional, mixed, and unclassified. Twenty-nine subcategories are defined by further classifying the main categories according to anticyclonic, cyclonic, directional, and strength of flow considerations. Both Grosswetterlagen and the Lamb Catalogue have been retroactively created through the 19th century.
Subjective schemes such as these have several benefits. The investigator is in full control of the process and classification, which can be performed without access to significant computer resources. The classification system can thus be tailored precisely to the researcher""s needs. Unfortunately, these main attractions of manual classifications are also their drawbacks. These schemes can be difficult to export to other locations, and are also quite time consuming. Subjectivity can become excessive: different researchers will not necessarily agree on classifications for a given day; thus these schemes are not replicable.
The computer revolution of recent decades has resulted in the development of many more synoptic classification methods, especially automated ones. One such method is called correlation-based map patterns. The ultimate product is similar to Muller""s, but the human decision is replaced by an automatic grouping based on similarity of (usually) sea-level pressure patterns in the region of interest. Correlation coefficients are computed between all map pairs, comparing pressure values at corresponding grid points. The first key day is the map with the highest average correlation with other maps. Maps above a certain threshold of correlation with the key day are then grouped and removed from the pool. The process then iterates; maps can later migrate to other groups if their correlation is higher with a newly selected key day. Much care must be taken in defining the several necessary parameters, namely the minimum correlation threshold and the number of categories desired. This method, unlike the manual method, assures the reproducibility of classification, and has been used for a variety of research. Of course, this method can easily produce categories which do not conform to investigator needs. Within-category variability can often be significant and reduce potential benefit from the system, and slight changes in input thresholds can result in significantly different results.
Another very common group of synoptic classification methods in recent times is eigenvector-based. Typically this involves two steps: a xe2x80x9creduction of variablesxe2x80x9d and clustering of those reduced variables. Representative of this method is the Temporal Synoptic Index (TSI). The TSI begins with a reduction of variables via principal components analysis (PCA). PCA transforms the original variables into new variables, each of which is orthogonal to all others. Orthogonal variables by definition have no collinearity between variables [cov (x,y)=0], eliminating a common feature of many meteorological datasets. Each transformed variable has an associated eigenvalue, a representation of the total variance of the original variables it explains. A small subset of transformed variables can then be chosen (in the case of the TSI, those with eigenvalue greater than 1) to explain the majority of the original variability, reduce the number of calculations and effects of collinearity.
Once the data are xe2x80x9creducedxe2x80x9d, a cluster analysis (CA) of daily transformed variable values is performed. TSI originally employed Ward""s algorithm, although many similar methods are available. These methods generally have thresholds which limit dissimilarity between groups (in the case of Ward""s algorithm, by minimizing the sum of squares between a particular day and the group mean). The user must manually limit clusters to a number which theoretically represents a relative maximum of among-group variance to within-group variance, but in practice represents an approximation of the number of groups expected or desired.
The TSI and fellow PCA/CA methods have been used to assess a large number of problems. from heat-stress related mortality to interpolation of missing values in a data set. These methods are commonly applied for their ease of use, reproducibility, and general ease of interpretation of results. The main drawback to this system is the lack of comparability between stations. Most of these methods are applied to only one station (or, in some cases one region) at a time, and comparison of results from station to station is not feasible, as each station may have a different number of clusters representing different conglomerations. One solution has been to turn the time dimension into space to perform a spatial cluster analysis. While results on some days were favorable, on others spatial patterns were irregular, and the need of redoing the entire procedure for each day reduced its practicality.
Having reviewed the benefits and advantages of both manual and automated methods, it seems intuitive that a valuable synoptic methodology could be derived by combining the two methods into a hybrid scheme. Attempts at hybrid schemes have been undertaken by relatively few researchers. One such scheme classifies air masses over the north central United States. Initial development is subjective: six air masses are identified, and 85-kPa temperatures and dew points for each air mass are taken from days when the air mass is clearly known by virtue of trajectory. Normal curves are then derived for each air mass based on the partial collectives technique, which assumes the overall frequency distribution of a given parameter is comprised of several superimposed normal curves. This automated segment then produces limits of parameter values for each air mass for each station-month. In order to prevent borderline days from being misclassified, a xe2x80x9ctransition zonexe2x80x9d is used as a buffer between air masses.
Another hybrid procedure produces map classifications. That procedure initially classifies 12 years of daily sea-level pressure maps for the eastern US into ten distinct classifications and an unclassifiable group. A mean pressure field is calculated for each of the classifications. The mean fields then serve as keydays in the correlation-based method outlined above; the most subjective segment of the keyday procedure the number of keydays, is not an issue as the number of groups is already chosen. A correlation based threshold is then used to assign all days into one map type. Results show that on fewer than half of the days do the manual and hybrid procedures match, and while aggregated group comparisons show consistency, in smaller groups of data the disparity between manual and hybrid becomes readily apparent.
The first version of the Spatial Synoptic Classification (SSC1) system is a hybrid system which is redeveloped for use in this research. A full description of its original manifestation along with modifications for the present invention will be set forth below.
Some meteorological concepts will now be introduced.
Teleconnections are the linkages over large distances of what intuitively seem to be disconnected weather anomalies. Some teleconnections are observed as sea-level pressure anomalies, which have a direct effect on low-level wind and other surface meteorological conditions. Others are more appropriately assessed by geopotential height patterns above the surface (usually at 50 or 70 kPa); these patterns (which, by geostrophic theory, approximate wind flow patterns) are the steering mechanism for the atmosphere, differentially advecting air masses into different regions. A solid connection therefore exists between surface meteorological conditions and height patterns, although the relationship is variable through time and space.
Many teleconnection studies relate climate anomalies to various xe2x80x9cseesawxe2x80x9d mechanisms, or weather cycles, throughout the world. One of the most potent, and after the event of 1997-98, the most well-known, is El Nixc3x1oxe2x80x94Southern Oscillation (ENSO). There are many more possible climate controls, however, from the Quasi-Biennial Oscillation (100 yr) to the Luni-Solar cycle (101 yr), and orbital (Milankovich) parameter oscillations (102-104 yr).
Climate variability is a topic of critical interest to many contemporary researchers, who are trying to distinguish xe2x80x9cnaturalxe2x80x9d variability from that which is suggestive of human involvement. While scientific certainty is lacking in the overall debate on human-induced climate change, it is well established that a fair amount of the variability in the climate can be explained by weather cycles. For example, it has been shown that 72 percent of Northern Hemisphere January temperature variability can be accounted for by the variations in six teleconnective indices.
The El Nixc3x1oxe2x80x94Southern Oscillation (ENSO) phenomenon is comprised of two synchronous (and synergistic) componentsxe2x80x94the oceanic (El Nixc3x1) and the atmospheric (Southern Oscillation). Both components have been known to exist since the early part of the 20th century; it was not until much later that their connection was realized. The term xe2x80x9cEl Nixc3x1oxe2x80x9d originally designated the cessation of local up welling (and inherent sea surface temperature rise) associated with a weak westerly current along the Peruvian and Ecuadorian coastline, regularly observed near Christmas time (hence El Nixc3x1, xe2x80x9cThe Christ Childxe2x80x9d). Irregularly every 2 to 10 yr, this warming is particularly strong, and over the course of subsequent months anomalously high sea-surface temperatures (1 to 5xc2x0 C. above average) and a deepened thermocline intensify and spread westward to the International Date Line, and along American coasts from Mexico to Southern Peru. Events are by no means regular or typical; although in general, peak warming occurs near the following Christmas, after which it dissipates. While many definitions exist, it is this entire warming event which is generally labeled El Nixc3x1o.
The Southern Oscillation is the atmospheric companion to El Nixc3x1o. It involves a redistribution of atmospheric mass above the Pacific Basin, and a disruption of the Walker Circulation. The Walker Circulation is the east-west atmospheric circulation above the equatorial Pacific, driven by large-scale sea surface temperature (SST) anomalies. The eastern edge of this circulation is the South American Pacific coast, where upwelling results in cooler ocean temperatures;. the western edge is near Indonesia, where SSTs are among the highest in the world. The convection in this region resulting from the high SSTs causes lower pressure west, and higher pressure east. Through the pressure gradient force, these pressure anomalies produce low-level easterly winds, which rise over the Western Pacific, return west, and sink over the Eastern Pacific.
This xe2x80x9ctypicalxe2x80x9d pattern generally breaks down during an El Nixc3x1o event. The aforementioned SST anomalies decrease (or even reverse) the temperature gradient, disrupt the Walker circulation, and shift the center of convection from Indonesia to the Central Equatorial Pacific. Many subsequent teleconnections are set in motion; those related to North America are discussed below.
This combination oft he above atmospheric and oceanic events is hereafter called a Warm Event. In addition, there are Cold Events, alternately known as La Nixc3x1a or LNSO events. Cold Events have received less attention than Warm Events, since the east-west SST gradient and Walker Circulation are merely enhanced but do not change sign. Convection increases in the western Pacific and decreases in the central and eastern Pacific. In general, Cold Events occur somewhat less frequently than Warm Events, although it is common for a Cold Event to occur during the year following a strong Warm Event.
One or both of two common criteria are used to determine the occurrence of a Cold or Warm Event; an atmospheric response, and an oceanic response. The principal atmospheric index is the Southern Oscillation Index (SOI). The SOI, generally performed on monthly means, represents the standardized difference in sea-level pressure between Papeete, Tahiti, in the Central Pacific (17xc2x0 S, 150xc2x0 W), and Darwin, Northern Territory, on the northern coast of Australia (12xc2x0 S, 131xc2x0 E). During Warm Events, lower (higher) pressure at Tahiti (Darwin) results in a negative index; Cold Events are associated with a positive index value. An Event is usually said to be occurring when several (usually 6) consecutive months have SOI values above/below xc2x11.0.
Oceanic-based definitions of ENSO consist of SST anomalies from xe2x80x9caveragexe2x80x9d conditions for a given region of the Pacific. Many different regions have been defined over time; the most recent xe2x80x9coptimalxe2x80x9d region is the Nixc3x1o 3.4xe2x80x3 region, which is a box between 5xc2x0 N and 5xc2x0 S, 1xc2x0 W and 170xc2x0 W. This region is the centered on the area of peak correlation coefficient between SST and SOI (c. xe2x88x920.8).
A considerable amount of research has analyzed anomalies of temperature and precipitation in relation to ENSO events. Only a subset of those which have examined North America are examined here. As with anomalies associated with all teleconnections and weather cycles, it should be kept in mind that the anomalies are tendencies derived from many years"" data, and other year-to-year variability can mask much of the signal in any given year.
While the exact dimensions of the anomaly vary according to researcher, the most significant Warm Event thermal anomalies are above-average temperatures across much of Alaska, western and central Canada, the northwestern US, and California, from December through May. Some additional studies extend this anomaly into the Great Lakes region, across all of southern Canada to Newfoundland, or contain a distinct secondary region over Quebec, the Atlantic. Provinces, and Maine. Another anomaly commonly discovered is of below average temperatures in the southeastern US from October to May. In some studies this is limited to the immediate Gulf Coast, while others include a wider region, as wide as a swath from New Mexico to Virginia, and south to Cuba. Summertime anomalies are less significant both in magnitude and reliability; however, cooler than average temperatures are noted in the US Rockies and northern US Plains.
Cold Event research has been undertaken by fewer researchers; however, most agree on a near exact reversal of the anomaly in Alaska, Canada, and the northwestern US, with colder than average temperatures during winter and spring. No southeastern US winter and spring thermal anomaly appears in Cold Events, although a cooler than average July to June is observed in the Caribbean.
Precipitation anomalies with ENSO are more commonly studied. The most established North American anomaly during Warm Events is a wetter Gulf Coast region, during the same time period and covering the same extent as the cold anomaly mentioned above. Not far to the north, some studies show a considerable region of drier than average conditions during the same winter/spring in the Great Lakes and Ohio River Valley. A wetter northern Great Plains and Great Basin 1986) during summer have been noted. Some research has shown California precipitation to be higher during Warm Event winters, although this relationship is tenuous and dependent upon specific Pacific SST anomaly patterns. Summer monsoon precipitation in Arizona and New Mexico shows spatial shifts between Warm Events and Cold Events; Warm Events tend to produce most positive anomalies over northern New Mexico, while Cold Events result in a wetter West Central Arizona. Lastly, during summer and autumn, anomalous westerly winds in the middle troposphere above the Atlantic inhibit tropical cyclone formation there; tropical-related precipitation is thereby reduced along the Atlantic and Gulf Coasts.
Cold Events tend to produce opposite anomalies for most locations, although the teleconnection response is clearly non-linear in others. While the magnitude is generally smaller; the year-to-year variability in Cold Event anomalies are less than the Warm Event anomalies. The most significant Cold Event anomalies are a drier Gulf Coast and wetter Ohio River Valley during winter.
In contrast to ENSO, the Pacific North American (PNA) teleconnection pattern is not a phenomenon in itself, but rather a derived index of mid-tropospheric circulation (either 50 or 70 kPa). It features several xe2x80x9ccenters of actionxe2x80x9d: near Hawaii, the Aleutian Low, central Alberta, and the Florida Panhandle. These four centers are xe2x80x9cteleconnectedxe2x80x9d in that a positive geopotential height anomaly near Hawaii tends to be associated with a positive anomaly near Alberta, and negative anomalies in the Aleutian Low and Florida Panhandle. One common definition of the PNA index is:
PNA=1/3[xe2x88x92Z*(47.9xc2x0 N, 170.0xc2x0 W)+Z*(49.0xc2x0 N, 110.0xc2x0 W)xe2x88x92Z*(29.7xc2x0 N, 86.3xc2x0 W)],
where Z* signifies the standardized 70-kPa geopotential height anomaly. This index ignores the Hawaiian center, as do most formulations which arc used to assess North American climate anomalies. Positive PNA values (+PNA, also known as xe2x80x9cPNAxe2x80x9d) signify a more meridional flow over the North American continent. In winter, this generally means an amplification of the long-wave western North American ridge and eastern-North American trough which occur climatologically. Reverse PNA (xe2x88x92PNA, xe2x80x9cRPNAxe2x80x9d) results in a more zonal flow over the continent, with a damping of the aforementioned ridge-trough system.
A PNA pattern has been shown to be the first principal component of Northern Hemispheric circulation in January, and a major component during all times of year except summer. A connection is often made between PNA and ENSO, partially due to an intensification of the Rossby wave train near the Hawaii xe2x80x9ccenterxe2x80x9d by increased SSTs during ENSO Warm Events. However, a typical xe2x80x9cPNA responsexe2x80x9d has been observed in only half of Warm Event winters, and between 1947-1990, only 17 percent of SOI variability is explained by the PNA index.
The North Atlantic Oscillation (NAO) is similar to the PNA in that it is a Northern Hemispheric circulation index, and while it can be related to SST, it is primarily an atmospheric feature. Like the Southern Oscillation, the NAO represents a large-scale shift in atmospheric mass, and is generally observed via anomalies of sea-level pressure. The oscillation is between the two characteristic North Atlantic pressure centers: the Azores High, centered near the Azores (38xc2x0 N, 26xc2x0 W), and the Icelandic Low, centered between Greenland and Iceland (Lamb and Peppler 1987). The definition oft he NAO Index is usually the normalized difference in sea-level pressure between Ponta Delgada, Azores, and Akureyri, Iceland. A positive value of NAO (+NAO) signifies a stronger than average Icelandic Low and Azores High. Conversely, negative NAO values (xe2x88x92NAO) signify weaker than average pressure centers; with extreme negative NAO values, this can lead to a reversal of the typical pattern, with a weak high pressure center near Iceland.
The Quasi-Biennial Oscillation (QBO) is a well-documented reversal of winds in the stratosphere above the equator. Known since the 1950s, shifts (usually observed at 5 kPa) between the xe2x80x9cWest Phasexe2x80x9d and xe2x80x9cEast Phasexe2x80x9d occur with a periodicity averaging 28 mo, ranging between 21 and 33 mo. The West Phase plateaus at a maximum of near 10 m sxe2x88x921 for 10-20 mo, followed by a transition over several months to the East Phase. Easterly winds reach a stronger peak, near 20-25 m sxe2x88x921, yet persist for only 24 mo before a rapid transition back to West Phase. As the total cycle is slightly longer than 2 yr, there is no seasonality to the oscillation, although Kane (1992) notes a tendency for stronger accelerations between March and May.
The QBO in itself is not generally linked to climate anomalies, but rather modulates other teleconnections. The most important studies use the QBO to link the solar cycle with surface and lower tropospheric climate anomalies.
Two oft he oldest weather cycles explored are those related to the solar cycle and the luni-solar tide. Searches for their influence on climate date back two centuries, yet no widely-accepted explanation for the causal mechanisms of these cycles has ever been asserted. The luni-solar tide acts physically upon the Earth, causing slight acceleration and deceleration with a periodicity of 18.6 yr. The solar cycle is a manifestation of the 10-11 yr variation in sunspot activity, which alters the solar output by up to 0.1 percent.
While each individual weather cycle may have a large role in impacting climate, the synergy among the weather cycles can also be examined. One such study uses multiple linear regression to assess the effects of several teleconnection indices on meteorological conditions.
The vast majority of the studies focus on a surface response manifested in average temperature or total precipitation anomalies, often on a timescale of a month or longer. While valid and useful conclusions can be made from such parameters, they do not provide a full understanding of the effects of teleconnections or weather cycles. For example, the same xe2x80x9caveragexe2x80x9d month can be comprised of a month of all days with near average temperature or a month filled with two weeks of well-above average temperature and two weeks of well below average temperature. Precipitation anomalies can be even more misleading, as point estimates, particularly during convective season, are poor estimators of a regional precipitation pattern.
Using a synoptic classification scheme to assess climate variability can provide more and different information. Responses can be expressed in terms of changing air mass or pressure pattern frequencies, which can be more enlightening in terms of large-scale precipitation anomalies, or for biometeorological or agricultural purposes. Relatively few studies have devoted much effort to an assessment of this sort.
The damage to agriculture caused by hard freezes in Florida has prompted research into the relationship between these freezes and teleconnections. This can be thought of as a synoptic assessment of climate variability where only one air mass or map pattern is examined.
While secular trends in climate data have not been reviewed above, much research into climate variability via synoptic methods has focused on secular trends, under the guise of climate change detection.
The Spatial Synoptic Classification (SSC) is one such weather-typing scheme, developed in the mid-1990""s at the University of Delaware. The SSC is based on the identification of six different types of air masses across the North American continent, and at a station-by-station level, it assigns each day into one of those air masses, or a transition between types. It has been used for general climatological purpose as well as applications to pollution, health, and other weather phenomena. It is presently being adapted for use in heat watch warning systems at different locations throughout the world. For all its usefulness, however, the system does have several limitations, most notably its availability only during the winter and summer seasons.
The spatial synoptic classification, first generation (hereafter known as SSC1), is disclosed in L. S. Kalkstein et al, xe2x80x9cA New Spatial Synoptic Classification: Application to Air-Mass Analysis,xe2x80x9d International Journal of Climatology, Vol. 16, pp. 983-1004 (1996), and in J. S. Greene et al, xe2x80x9cQuantitative analysis of summer air masses in the eastern United States and an application to human mortality,xe2x80x9d Climate Research, Vol. 7, pp. 43-53 (1996), both of which are hereby incorporated by reference into the present disclosure. An overview of SSC1 will be provided with reference to FIGS. 1 and 2.
FIG. 1 shows the selection of seed days, which are days representing the typical meteorological character of each air mass at a location (locations typically being weather stations at airports). A determination of what types of synoptic events occur in the study area (step 101) and at that location (step 103), and of the meteorological character of each type of event (step 105), provide the knowledge of the synoptic events (step 107). Seed day selection criteria are developed, typically with reference to the selection criteria used at nearby locations (steps 109 and 111). The criteria are adjusted as needed (step 113), and in accordance with afternoon and diurnal meteorological observations, the seed days are selected (steps 115 and 117). It is then determined whether the selected seed days are acceptable, i.e., representative of the types of air masses under study (step 119). If not, the criteria are adjusted again (step 113).
Following the selection of acceptable seed days, discriminant function analysis is used to generated a linear function for each air mass from its group of seed days. The air masses are categorized as dry polar (DP), dry temperate (DM), dry tropical (DT), moist polar (MP), moist temperate (MM), and moist tropical (MT) (step 201). The seed days are selected as representing pure synoptic events (step 203) or transitions between synoptic events (step 205). Days other than seed days can then be classified as pure events (step 207) or transitional events (step 209). The resulting pure and transitional calendars are merged (step 211), and statistics are calculated for each type of synoptic event (step 213).
After analyzing many historical weather maps and climatologies, the developers of SSC1 decided that the traditional air mass lexicon mentioned in Chapter 2 (cP, cT, mP, mT) was too limited for application to the eastern half of the United States (the initial SSC classification region). In its place, six air mass types are defined:
1) Dry Polar (DP)
4) Moist Polar (MP)
2) Dry Moderate (DM)
5) Moist Moderate (MM)
3) Dry Tropical (DT)
6) Moist Tropical (MT)
Dry polar air is largely synonymous with the traditional cP air mass classification. It is characterized by cool or cold dry air, and for much of the continent, northerly winds. Skies typically feature little or no cloud cover. This air mass has its source in Northern Canada and Alaska, and is advected into the rest of North America by a cold-core anticyclone which emerges from the source region.
Dry Moderate or Dry Temperate air is mild and dry. This air mass has no traditional source region. In the eastern and central portions of North America, DM usually appears with zonal flow aloft, which permits air to traverse the Rocky Mountains, to dry and warm adiabatically. It is analogous to the Pacific air mass (Pa) identified by Schwartz (1991) and others. It can also be found over the southeastern US as polar air which has been brought back ashore after significant modification over the ocean. In the southwestern US desert areas where the character of the monsoon air mass does not reflect a true Moist Tropical air mass (see below), DM air can be identified (Sheridan 1997). In many cases, however, it merely reflects a significantly modified DP air mass or a mixture of Dry Tropical and Moist Tropical, or Dry Polar and Moist Tropical, influence.
Dry Tropical air is associated with the hottest and driest conditions, and clear skies. It is analogous to the traditional cT designation. It appears via two scenarios. Most commonly, it is present or advected (usually via surface anticyclone) from its source region, the deserts of the southwestern US and northwestern Mexico. It can also be produced by violent downsloping winds, where rapid compressional heating can produce desert-like conditions. The Chinook, common in the US and Canadian Rockies, and the Santa Ana winds of California, are two examples of this.
Moist Polar air is a large subset of the mP air mass. Weather conditions are cool, cloudy, and humid, often with light precipitation. This can appear via inland advection of air from the North Pacific or North Atlantic. It can also arise when there is frontal overrunning well to the south, or when a DP air mass acquires moisture while traversing a cool water body (the Great Lakes being the primary example).
Moist Moderate or Moist Temperate air is warmer and more humid than MP air, and also cloudy. This can form either as a modified mP air mass, or independently, south of MP air nearer a warm front. During summer, it can also occur under mT influence on days with high cloud cover (hence lowering the temperature).
Moist Tropical air is analogous to mT; it arrives in North America either via the Gulf of Mexico or tropical Pacific Ocean. It is found in the warm sector of a mid-latitude cyclone, and on the western side of a surface anticyclone. This air is warm and very humid, cloudy in winter and partly cloudy in summer. Convective precipitation is quite common in this air mass, especially in summer.
These six air masses, along with a transitional (TR) situation, which represents a day in which one air mass yields to another, were not altered during the SSC redevelopment. Quantification of the typical conditions of these air masses can be found in Chapter 4.
The foundation of the SSC rests upon the proper identification of the character of each air mass for a particular location. This is accomplished by the selection of Seed days. A seed day is an actual day in a station""s period of record which represents the xe2x80x9ctypicalxe2x80x9d meteorological characteristics of a particular air mass at that location. xe2x80x9cExtremexe2x80x9d days (e.g., the coldest DP days, most humid MT days) are avoided as they would bias the sample.
In order to obtain seed days, first these typical characteristics need to be quantified. Ranges of several different meteorological variables are-, specified, and a computer program extracts from a station""s period of record all the days during a specified time of year which satisfy these criteria.
For the SSC1, seven different criteria were used in seed day identification:
afternoon (16 h EST) temperature.,
afternoon (16 h EST) dew point,
afternoon (16 h EST) cloud cover,
afternoon (16 h EST) wind direction,
afternoon (16 h EST) dew point depression,
diurnal temperature range (among values at 04, 10, 16, 22 h EST), and
six-hour dew point change (among same values).
After the seed day selection was complete, weather maps for the selected days were then analyzed to confirm that the days chosen did indeed represent the particular air mass for the given location. If the days were deemed to be non-representative, the seed day criteria would be adjusted and the procedure repeated.
Seed day criteria were specified individually for each station analyzed. As the spatial cohesiveness of the SSC is paramount, however, much effort was placed in assuring that neighboring stations have similar criteria for the same air mass, adjusting for local climatic factors. Different sets of seed criteria were selected for winter (December, January, and February) and summer (June, July, and August), and seeds were chosen from the period 1961-1990. At least 30 seed days represent each air mass in most locations, although rarely occurring air masses may by necessity yield fewer seed days.
In short, the SSC1 is a hybrid categorization system employing both manual and automated segments. The initial stage requires manual identification of air masses; once this is completed, an automated classification of days then occurs. The system was originally developed using discriminant function analysis for classification purposes.
Once seed days are selected, the next component of the SSC takes in the seed days and outputs an air mass category for every day in a station""s period of record. The SSC1 utilized discriminant analysis for evaluation purposes (see Kalkstein et al. 1996 for a detailed description). Discriminant analysis is designed to measure the differences among multiple groups of objects (here, air masses) with respect to multiple variables simultaneously. The objective. is to assign new objects, to the predetermined groups using particular classification rules. The rules are the discriminant functions. Linear discriminant function analysis, used here, assumes multivariate normality, and equal covariance matrices within and among groups, although the procedure is still robust when these assumptions are relaxed (Klecka 1980).
Discriminant analysis uses, the covariance matrix and mean values of the variables to develop classification functions, to determine into which predetermined air mass category a particular new day belongs. A set of linear equations of the following form is developed:
hk=bk0+bk1xc3x97X1+bk2xc3x97X2+ . . . +bkpxc3x97Xp,xe2x80x83xe2x80x83(1)
where h(k) is the value of the discriminant function for group (air mass) k, X is the value of each of the p variables (e.g., temperature), and b(kj) are modification coefficients based on the true group variability. These coefficients are determined by:                                           b                          k              ⁢                              xe2x80x83                            ⁢              i                                =                                    (                                                n                  t                                -                g                            )                        xc3x97                                          ∑                                  j                  =                  1                                p                            ⁢                                                a                                      i                    ⁢                                          xe2x80x83                                        ⁢                    j                                    *                                xc3x97                                  X                                      j                    ⁢                                          xe2x80x83                                        ⁢                    k                                                                                      ,                            (        2        )            
where b(ki) is the coefficient for variable i in the equation corresponding to group k, X(jk) is the value of the variable, n(t) is the total number of cases over all g groups, and a(ij)* is an element from the inverse of the covariance matrix (A). A is defined by:                                           a                          i              ⁢                              xe2x80x83                            ⁢              j                                =                                    ∑                              k                =                1                            g                        ⁢                                          ∑                                  m                  =                  1                                                  n                  t                                            ⁢                                                (                                                            X                                              i                        ⁢                                                  xe2x80x83                                                ⁢                        k                        ⁢                                                  xe2x80x83                                                ⁢                        m                                                              -                                                                  X                        _                                                                    i                        ⁢                                                  xe2x80x83                                                ⁢                        k                                                                              )                                ⁢                                  (                                                            X                                              i                        ⁢                                                  xe2x80x83                                                ⁢                        j                        ⁢                                                  xe2x80x83                                                ⁢                        m                                                              -                                                                  X                        _                                                                    j                        ⁢                                                  xe2x80x83                                                ⁢                        k                                                                              )                                                                    ,                            (        3        )            
where n(k) is the number of elements in group k, X(ik) is the mean value in the kth group, and X(ikm) is the value of variable i for case m in group k. The inverse oft he matrix is then computed to determine the a(ij)* values. The constant term in (1), b(k0), is defined as:                               b          k0                =                              -            0.5                    xc3x97                                    ∑                              j                =                1                            p                        ⁢                                          b                                  k                  ⁢                                      xe2x80x83                                    ⁢                  j                                            xc3x97                                                X                                      j                    ⁢                                          xe2x80x83                                        ⁢                    k                                                  .                                                                        (        4        )            
A distinct discriminant function is calculated for each group and evaluated for each day. The day is, then classified into the group (air mass) with the highest score (highest h(k).
Days on which a transition between one air mass and another occurs originally received an incorrect classification, as the system heretofore described only evaluates pure air masses. A second stage was therefore developed to account for this problem. Two new seed day groups were selected: one to represent pure air mass days, and the other to represent transitional days. Instead of the twelve variables from Table 1 below, only two variables were used: dew point change and sea level pressure change, both highly indicative of transitional situations. If both of these variables were twice the average, then it was designated a transition seed day. The second procedure is then run; a day which is classified as non-transitional keeps its original designation; one which is, becomes a Transition (TR) day.
In its original form, the SSC1 possesses several limitations. As mentioned, the system was only developed for winter and summer. Selecting seed days with one set of criteria over an entire transitional season does not work. While the character of a typical DP day does not change significantly between December and February, from March to May, meteorological conditions undergo rapid change. Further, while air mass frequencies with SSC1 are spatially homogeneous, air mass xe2x80x9cmatchesxe2x80x9d (frequency of coincidence of air mass at neighboring stations) on a daily basis were less than expected. The seed day selection process was not streamlined to where a new station could easily be incorporated into the system. Entirely new seed day criteria (and hence seed days) would need to be assigned for each new station.
From the foregoing, it will be readily appreciated that a need exists in the art for a spatial synoptic classification which can accurately handle the transition seasons and which can handle multiple weather stations in a computationally efficient manner.
The first goal of the present invention is the redevelopment of the SSC. The redevelopment includes both sweeping changes to both the methodology and the implementation of the SSC for a much larger number of stations. The changes to the methodology render the system operations year-round, more capable of incorporating a new station, more easily understood, and with an increased cohesiveness among stations. In addition, it allows for a daily calendar to be construction and updated in real-time form. The implementation oft he system now extends its availability for 160 stations in the contiguous US, to 328 stations in all of the US and Canada. Character and frequency of all air masses at all stations have been assessed.
The second goal is to use the redeveloped SSC for two climate-related applications: as a distinct method of climate change assessment, and to help understand the local climatological impact of various teleconnections or weather cycles, of which El Nixc3x1o is one example. To meet this second goal, the difference in the frequency and character of the six air masses among different phases of the weather cycles are assessed, along with the air mass frequency and character trends over time. The spatial continuity of any anomalies is determined, and the results should provide more insight into the effect of weather cycles and the understanding of secular trends than the mere precipitation of temperature anomalies usually ascribed.
To achieve the above and other goals, the spatial synoptic classification (SSC1) is modified to form a new spatial synoptic classification (SSC2) by taking seed days within sliding periods so that seed days are selected from the warmest and coldest periods of the year and two intermediate periods. Artificial seed days are produced to represent typical weather patterns on other days of the year. From the selected seed days and the artificial seed days, a classification technique is developed to categorize a weather type for any day. Once the seed days are selected for a weather station, those seed days can be used to select seed days for nearby weather stations.